Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases.
Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases.
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DOI：
10.1186/s11689-022-09438-w
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发表时间：
2022-05-02
影响因子：
4.9
通讯作者：
中科院分区：
文献类型：
--
作者：
智力和发育障碍（IDDs），如唐氏综合征，脆性X染色体综合征，雷特综合征和自闭症谱系障碍，通常在出生或幼儿期表现出来。缺碘症的特点是智力和适应功能严重受损，遗传和环境因素都是缺碘症生物学的基础。IDDs的分子和遗传分层仍然具有挑战性，主要是由于重叠因素和共病。高通量测序、成像和大规模记录行为数据的工具的进步极大地增强了我们对某些IDD的分子、细胞、结构和环境基础的理解。在“大数据”革命的推动下，人工智能（AI）和机器学习（ML）技术为计算生物学带来了全新的范式转变。显然，ML驱动的临床诊断方法有可能增强使用症状和外部观察的经典方法，希望推动个性化治疗计划的发展。因此，ML技术的综合分析和应用直接关系到IDDs的发现。ML在IDDs中的应用可以潜在地改善筛查和早期诊断，促进我们对合并症复杂性的理解，并加速临床研究和药物开发的生物标志物的鉴定。五十多年来，IDDRC网络一直支持美国各地中心的研究人员，所有人都在努力了解IDD背后各种因素之间的相互作用。在这篇综述中，我们介绍了快速增长的多模态数据类型，重点介绍了采用ML技术阐明IDDs相关因素和生物学机制的示例研究，以及ML技术及其在IDDs和其他神经系统疾病中的应用的最新进展。我们讨论了各种分子，临床和环境数据收集模式，包括遗传，成像，表型和行为数据类型，沿着存储和共享这些数据的多个存储库。此外，我们概述了机器学习算法的一些基本概念，并提出了我们对需要填补的具体空白的看法，例如，在IDD诊所中可靠地实施基于ML的诊断技术。我们预计，这篇综述将指导研究人员制定基于AI和ML的方法来调查IDDs和相关疾病。
Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the “big data” revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.
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影响因子：
6.2
作者：
Andrews SV;Sheppard B;Windham GC;Schieve LA;Schendel DE;Croen LA;Chopra P;Alisch RS;Newschaffer CJ;Warren ST;Feinberg AP;Fallin MD;Ladd-Acosta C
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作者：
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DOI：
10.1108/amhid-10-2015-0050
发表时间：
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影响因子：
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作者：
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通讯作者：
Salvador-Carulla, Luis